Methods Inf Med - A generic framework for modeling brain deformation as a constrained parametric optimization problem to aid non-diffeomorphic image registration in brain tumor imaging.


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JECTIVES: In the present paper a novel computational framework for modeling tumor induced brain deformation as a biophysical prior for non-rigid image registration is described. More precisely, we aim at providing a generic building block for non-rigid image registration that can be used to resolve inherent irregularities in non-diffeomorphic registration problems that naturally arise in serial and cross-population brain tumor imaging studies due to the presence (or progression) of pathology.METHODS: The model for the description of brain cancer dynamics on a tissue level is based on an initial boundary value problem (IBVP). The IBVP follows the accepted assumption that the progression of primary brain tumors on a tissue level is governed by proliferation and migration of cancerous cells into surrounding healthy tissue. The model of tumor induced brain deformation is phrased as a parametric, constrained optimization problem. As a basis of comparison and to demonstrate generalizability additional soft constraints (penalties) are considered. A back-tracking line search is implemented in conjunction with a limited memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) method in order to handle the numerically delicate log-barrier strategy for confining volume change.RESULTS: Numerical experiments are performed to test the flexible control of the computed deformation patterns in terms of varying model parameters. The results are qualitatively and quantitatively related to patterns in patient individual magnetic resonance imaging data.CONCLUSIONS: Numerical experiments demonstrate the flexible control of the computed deformation patterns. This in turn strongly suggests that the model can be adapted to patient individual imaging patterns of brain tumors. Qualitative and quantitative comparison of the computed cancer profiles to patterns in medical imaging data of an exemplary patient demonstrates plausibility. The designed optimization problem is based on computational tools widely used in non-rigid image registration, which in turn makes the model generally applicable for integration into non-rigid image registration algorithms.

Resumo Limpo

jectiv present paper novel comput framework model tumor induc brain deform biophys prior nonrigid imag registr describ precis aim provid generic build block nonrigid imag registr can use resolv inher irregular nondiffeomorph registr problem natur aris serial crosspopul brain tumor imag studi due presenc progress pathologymethod model descript brain cancer dynam tissu level base initi boundari valu problem ibvp ibvp follow accept assumpt progress primari brain tumor tissu level govern prolifer migrat cancer cell surround healthi tissu model tumor induc brain deform phrase parametr constrain optim problem basi comparison demonstr generaliz addit soft constraint penalti consid backtrack line search implement conjunct limit memori broydenfletchergoldfarbshanno lbfgs method order handl numer delic logbarri strategi confin volum changeresult numer experi perform test flexibl control comput deform pattern term vari model paramet result qualit quantit relat pattern patient individu magnet reson imag dataconclus numer experi demonstr flexibl control comput deform pattern turn strong suggest model can adapt patient individu imag pattern brain tumor qualit quantit comparison comput cancer profil pattern medic imag data exemplari patient demonstr plausibl design optim problem base comput tool wide use nonrigid imag registr turn make model general applic integr nonrigid imag registr algorithm

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